| dc.contributor.author | Sapin, E | |
| dc.contributor.author | Keedwell, EC | |
| dc.contributor.author | Frayling, T | |
| dc.date.accessioned | 2016-03-31T10:25:09Z | |
| dc.date.issued | 2013-07-10 | |
| dc.description.abstract | In this paper an ant colony optimisation approach for the discovery of gene-gene interactions in genome-wide association study (GWAS) data is proposed. The subset-based approach includes a novel encoding mechanism and tournament selection to analyse full scale GWAS data consisting of hundreds of thousands of variables to discover associations between combinations of small DNA changes and Type II diabetes. The method is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover combinations that are statistically significant and biologically relevant within reasonable computational time. | en_GB |
| dc.description.sponsorship | The work contained in this paper was supported by an
EPSRC First Grant (EP/J007439/1).
This study makes use of data generated by the Wellcome
Trust Case Control Consortium. A full list of the inves-
tigators who contributed to the generation of the data is
available from http://www.wtccc.org.uk. Funding for the
project was provided by the Wellcome Trust under award
076113. | en_GB |
| dc.identifier.citation | GECCO '13 Proceedings of the 15th annual conference on Genetic and evolutionary computation, pp. 295-302 | en_GB |
| dc.identifier.doi | 10.1145/2463372.2463410 | |
| dc.identifier.uri | http://hdl.handle.net/10871/20897 | |
| dc.language.iso | en | en_GB |
| dc.publisher | Association for Computing Machinery (ACM) | en_GB |
| dc.relation.url | http://dl.acm.org/citation.cfm?id=2463372.2463410 | en_GB |
| dc.title | Subset-Based Ant Colony Optimisation for the Discovery of Gene-Gene Interactions in Genome Wide Association Studies | en_GB |
| dc.type | Conference paper | en_GB |
| dc.identifier.isbn | 978-1-4503-1963-8 | |
